Drug related deaths in the United States of America
Drug related deaths in the US - A case study
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Overview and Motivation
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Summary of study
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Let´s jump right in an take a look at the total number of deaths by drugs scaled for 10 000 inhabitants per state.
We scale the data so the relationship between the number of deaths become equal for the number of inhabitants in every state. Then we divide all of the states into four regions:
Northeast

Midwest

South

West

Deaths by drugs for all states compared to one another in 2016:

How did they rank in 2017?
|
State
|
Deaths per 10 000 inhabitant
|
Rank
|
|
District of Columbia
|
67.584604
|
1
|
|
West Virginia
|
63.899986
|
2
|
|
Ohio
|
52.540842
|
3
|
|
Pennsylvania
|
50.511744
|
4
|
|
Maryland
|
45.563919
|
5
|
|
Kentucky
|
42.629676
|
6
|
|
Delaware
|
41.814790
|
7
|
|
New Hampshire
|
41.138074
|
8
|
|
Massachusetts
|
37.697788
|
9
|
|
Rhode Island
|
37.616585
|
10
|
|
Connecticut
|
35.098534
|
11
|
|
Maine
|
33.640066
|
12
|
|
Florida
|
33.470295
|
13
|
|
Tennessee
|
31.959776
|
14
|
|
Indiana
|
31.790156
|
15
|
|
New Jersey
|
31.742316
|
16
|
|
Michigan
|
31.503831
|
17
|
|
Nevada
|
29.179073
|
18
|
|
New Mexico
|
28.907077
|
19
|
|
Missouri
|
27.873136
|
20
|
|
North Carolina
|
26.844881
|
21
|
|
Louisiana
|
26.687018
|
22
|
|
Vermont
|
26.184265
|
23
|
|
Oklahoma
|
25.624791
|
24
|
|
Arizona
|
25.016141
|
25
|
|
South Carolina
|
24.457369
|
26
|
|
Utah
|
24.263073
|
27
|
|
Illinois
|
24.085293
|
28
|
|
Wisconsin
|
23.718754
|
29
|
|
Colorado
|
21.483269
|
30
|
|
Alaska
|
21.235613
|
31
|
|
Virginia
|
20.664846
|
32
|
|
Alabama
|
19.759101
|
33
|
|
Washington
|
17.855062
|
34
|
|
Georgia
|
17.736893
|
35
|
|
Idaho
|
16.348825
|
36
|
|
Hawaii
|
16.195716
|
37
|
|
Arkansas
|
16.135175
|
38
|
|
Minnesota
|
15.290441
|
39
|
|
Wyoming
|
15.034998
|
40
|
|
Oregon
|
14.963396
|
41
|
|
California
|
14.773633
|
42
|
|
New York
|
14.671981
|
43
|
|
Montana
|
13.945833
|
44
|
|
North Dakota
|
13.132557
|
45
|
|
Mississippi
|
13.078333
|
46
|
|
Kansas
|
13.030601
|
47
|
|
Texas
|
12.954382
|
48
|
|
Iowa
|
12.888233
|
49
|
|
South Dakota
|
10.545914
|
50
|
|
Nebraska
|
8.091983
|
51
|
What is happening in district of columbia???
This is some serious numbers. The total average drug deaths per state for 2015 is 11 585 and worse, it increased to 15 856 in 2017. This is an increase of 36.87% from 2015 to 2017, implefying that it is a serious problem in the US. The total number of deaths by drugs was 590 825 in 2015, 682 084 in 2016 and 808 661 in 2017. This equals a total of 2 081 570 people just for the three years this case study is studying. That´s the same as the total population of Slovenia to put things in perspective. Wiped out over three years.
But how many drug deaths do we find compared to all deaths?
A small percentage: 1.9 % of all deaths in average in the US was drug related in 2015 2.2 % of all deaths in average in the US was drug related in 2016 2.45 % of all deaths in average in the US was drug related in 2017
Low income and high unemployment will result in high overdose rate?
2016 numbers
Our assupmption is that low income and high unemployment equals high od-rate. So we take a look at the top 10 states of high od-rate, lowest income and highest unemployment.
|
State
|
Deaths pr 10000 inhabitants
|
Rank
|
|
West Virginia
|
53
|
1
|
|
New Hampshire
|
39
|
2
|
|
Ohio
|
39
|
3
|
|
District of Columbia
|
38
|
4
|
|
Rhode Island
|
38
|
5
|
|
Kentucky
|
36
|
6
|
|
Pennsylvania
|
36
|
7
|
|
Massachusetts
|
35
|
8
|
|
Maryland
|
34
|
9
|
|
Connecticut
|
30
|
10
|
|
|
State
|
Median household income 2016
|
Rank
|
|
Mississippi
|
40528
|
51
|
|
Arkansas
|
42336
|
50
|
|
West Virginia
|
42644
|
49
|
|
Alabama
|
44758
|
48
|
|
Kentucky
|
44811
|
47
|
|
Louisiana
|
45652
|
46
|
|
New Mexico
|
45674
|
45
|
|
Tennessee
|
46574
|
44
|
|
South Carolina
|
46898
|
43
|
|
Oklahoma
|
48038
|
42
|
|
|
State
|
The unemployment rate in percent of states labor force 2016
|
Rank
|
|
Alaska
|
6.9
|
51
|
|
New Mexico
|
6.7
|
50
|
|
District of Columbia
|
6.1
|
48
|
|
West Virginia
|
6.1
|
48
|
|
Louisiana
|
6.0
|
47
|
|
Alabama
|
5.9
|
46
|
|
Illinois
|
5.8
|
44
|
|
Mississippi
|
5.8
|
44
|
|
Nevada
|
5.7
|
43
|
|
California
|
5.5
|
42
|
|
As we can se this is not allways the case. West Wirginia stands out and is represented badly in all three categories. The people working in DC make good money, but both unemployment and od-rate are high. Kentucky is represented in the table for low income. Maryland is the states with highest income the last couple of years by a solid margin, and is the 9th worst place for od in the country. Othervise the similarities was not as strong as expected.
But high income and low unemployment would equal low od-rate right? Not for Maryland thats for sure.
| Nebraska |
7.5 |
1 |
| South Dakota |
9.3 |
2 |
| North Dakota |
10.9 |
3 |
| Texas |
11.7 |
4 |
| Iowa |
11.9 |
5 |
| New York |
12.6 |
6 |
| Kansas |
13.0 |
7 |
| Mississippi |
13.2 |
8 |
| Minnesota |
13.9 |
9 |
| Montana |
14.2 |
10 |
|
| Maryland |
76067 |
1 |
| Alaska |
74444 |
2 |
| New Jersey |
73702 |
3 |
| District of Columbia |
72935 |
4 |
| Hawaii |
71977 |
5 |
| Connecticut |
71755 |
6 |
| Massachusetts |
70954 |
7 |
| New Hampshire |
68485 |
8 |
| Virginia |
66149 |
9 |
| California |
63783 |
10 |
|
| New Hampshire |
2.9 |
1 |
| Hawaii |
2.9 |
1 |
| South Dakota |
3.0 |
3 |
| North Dakota |
3.1 |
4 |
| Nebraska |
3.1 |
4 |
| Vermont |
3.2 |
6 |
| Colorado |
3.3 |
7 |
| Utah |
3.4 |
8 |
| Iowa |
3.6 |
9 |
| Maine |
3.8 |
10 |
|
What about the states with High(good) and low(bad) temperatures?
| Florida |
22 |
1 |
| Hawaii |
21 |
2 |
| Louisiana |
19 |
3 |
| Texas |
18 |
4 |
| Georgia |
18 |
5 |
| Mississippi |
17 |
6 |
| Alabama |
17 |
7 |
| South Carolina |
17 |
8 |
| Arkansas |
16 |
9 |
| Arizona |
16 |
10 |
|
| Alaska |
-3.0 |
1 |
| North Dakota |
4.7 |
2 |
| Maine |
5.0 |
3 |
| Minnesota |
5.1 |
4 |
| Wyoming |
5.6 |
5 |
| Montana |
5.9 |
6 |
| Vermont |
6.1 |
7 |
| Wisconsin |
6.2 |
8 |
| New Hampshire |
6.6 |
9 |
| Michigan |
6.9 |
10 |
|
blablabla
## [1] 0.87
## [1] 0.25
## [1] 0.012
## [1] 0.32





Remember that number of incidents do not equal number of death, since people could be affected by several drugs and all of them would be registerd.
What will a linear regression model say about OD dependent on income, weather and unemployment?
##
## Call:
## lm(formula = OD16_relation ~ Median_income16 + PrecipitationMM +
## Clear_days + Rate2016, data = comparedata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.001323 -0.000648 -0.000169 0.000505 0.002511
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.35e-04 1.45e-03 0.23 0.818
## Median_income16 1.02e-08 1.48e-08 0.69 0.496
## PrecipitationMM 4.60e-07 3.84e-07 1.20 0.238
## Clear_days -3.18e-06 5.20e-06 -0.61 0.544
## Rate2016 2.68e-04 1.34e-04 2.01 0.051 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9e-04 on 45 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.145, Adjusted R-squared: 0.0687
## F-statistic: 1.9 on 4 and 45 DF, p-value: 0.126
Interactive map with summary of every state
Heatmap of drug ratio US
The BIG DATA
Since this is a case study, it would be unreasenable of us not to show you the data we have been working on as a part of this task. The data is a result of scraping, gathering, massaging and arranging multiple data sets with tens of thousand observations. This is the end result the task has been created with. See for yourself, and feel free to do your own calculations. The code for everything is on our github. Thanks for reading. - Ørjan, Preben and Daniel.